Neural Network Estimation of Tourism Climatic Index (TCI) Based on Temperature-Humidity Index (THI)-Jordan Region Using Sensed Datasets

ALDabbas, Ashraf; Gál, Zoltán; Buchman, Attila

doi:10.2478/cjece-2018-0019

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Climate plays a key role in the sustainability of the tourism sector of a country [1] [2]. Studies have shown that the climate change is considered one of the most affecting factors in decision making for the selection of tourist destinations during holidays and other leisure activities [3]. Among several metrics and indexes that have been developed to study the influence of climate change in the field of tourism is the Tourist Climate Index (TCI).…”

Section: Introductionmentioning

confidence: 99%

Developing South African Tourism Climate Index

Krishnannair

Nandutu

Ali

et al. 2022

Preprint

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Travel and Tourism as a sector has been resilient despite the diverse climatic conditions that affect tourists. Climatic conditions prevailing in the tourist destinations determine the number of tourists visiting those destinations. In 1985, Mieczkowski developed the Tourism Climate Index (TCI) to estimate the impact of climatic conditions on tourism. A mathematical model for Tourism Climate Index South African (TCISA) is proposed. Five climatic variables such as maximum temperature, minimum temperature, precipitation, cloud cover and wind speed are used as the input to the index to estimate the values of TCISA. The model for TCISA is validated using the climate data for the Durban region. Results show Durban as one of the popular tourist destinations due to its climate suitability for various tourist activities. This is comparable with the data from TripAdvisor. This study recommends the validation of the mathematical model by using more data points and climatic variables.

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Section: Introductionmentioning

confidence: 99%

Developing South African Tourism Climate Index

Krishnannair

Nandutu

Ali

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Deep learning (DL) [1] is protruded as a novel scope of machine learning, applied, and affecting several domains in our daily life such as medical image processing [2], prediction [3], mobile traffic classification [4], computer vision [5], computer networks [6]. DL's concept can be described as straightforward as the process of feature learning by machines as they ordinarily very well in the field of classification.…”

Section: Introductionmentioning

confidence: 99%

Cassini-Huygens mission images classification framework by deep learning advanced approach

ALDabbas

Gál

2021

IJECE

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Developing a deep learning (DL) model for image classification commonly demands a crucial architecture organization. Planetary expeditions produce a massive quantity of data and images. However, manually analyzing and classifying flight missions image databases with hundreds of thousands of images is ungainly and yield weak accuracy. In this paper, we speculate an essential topic related to the classification of remotely sensed images, in which the process of feature coding and extraction are decisive procedures. Diverse feature extraction techniques are intended to stimulate a discriminative image classifier. Features extraction is the primary engagement in raw data processing with the purpose of data classification; when it comes across the task of analysis of vast and varied data, these kinds of tasks are considered as time-consuming and hard to be treated with. Most of these classifiers are either, in principle, quite intricate or virtually unattainable to calculate for massive datasets. Stimulated by this perception, we put forward a straightforward, efficient classifier based on feature extraction by analyzing the cell of tensors via layered MapReduce framework beside meta-learning LSTM followed by a SoftMax classifier. Experiment results show that the provided model attains a classification accuracy of 96.7%, which makes the provided model quite valid for diverse image databases with varying sizes.

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Estimation of the spatial climate comfort distribution using tourism climate index (TCI) and inverse distance weighting (IDW) (case study: Fars Province, Iran)

Masoudi¹

2021

Arab J Geosci

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This article was conducted to perform a temporal and spatial analysis in order to identify suitable climatic regions for tourism. We investigated tourism climate conditions in Fars province from 2006 to 2016 using tourism climate index (TCI). Also, modified inverse distance weighting (IDW) interpolation is applied to generate the optimal spatial pattern of the TCI distribution. The relationship between the interpolation accuracy and a critical IDW parameter, called power value (β), was evaluated for optimization. The results revealed that during four months of May, April, October, and November, 70–83% of cities in Fars province show excellent and ideal climatic comfort. In the four months of July, December, January, and March, about 45–54% of Fars province provide good and very good conditions for tourism activities. The spatial distribution of TCI also shows that the cities in the northern part generally have the most desirable conditions during the hot season, while the southern cities of Fars province are more suitable for tourism during the cold season. Also, analysis of optimization steps demonstrated that power value (β) affects interpolation accuracy. As our study suggests, using the optimal power values (β) of 1 and 2 can lead to optimal spatial interpolation of the TCI distribution. Overall, we found IDW and TCI as reliable tools for assessing bioclimatic comfort conditions, considering β-value as an influential factor that should be evaluated to achieve optimal interpolation results.

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Neural Network Estimation of Tourism Climatic Index (TCI) Based on Temperature-Humidity Index (THI)-Jordan Region Using Sensed Datasets

Cited by 5 publications

References 12 publications

Developing South African Tourism Climate Index

Developing South African Tourism Climate Index

Cassini-Huygens mission images classification framework by deep learning advanced approach

Estimation of the spatial climate comfort distribution using tourism climate index (TCI) and inverse distance weighting (IDW) (case study: Fars Province, Iran)

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